https://doi.org/10.7892/boris.144019 | downloaded: 1.2.2022
Diskussionsschriften
Does subsidised temporary employment get the unemployment back to work?
An econometric analysis of two different schemes
Michael Gerfin, Michael Lechner Heidi Steiger
03-03 April 2003
Universität Bern
Volkswirtschaftliches Institut Gesellschaftstrasse 49 3012 Bern, Switzerland
Does subsidised temporary employment get the unemployed back to work ?
An econometric analysis of two different schemes
Michael Gerfin
Department of Economics, University of Bern and IZA, Bonn
Michael Lechner, Heidi Steiger
*Swiss Institute for International Economics and Applied Economic Research (SIAW) University of St. Gallen
First complete version: September 2002 Revised: April 2003
Date this version has been printed: 16 April 2003 Comments welcome
Addresses for correspondence
Michael Gerfin Michael Lechner, Heidi Steiger
Department of Economics Swiss Institute for International Economics and
University of Bern Applied Economic Research (SIAW), University of St. Gallen Gesellschaftsstr. 49, CH-3012 Bern Dufourstr. 48, CH-9000 St. Gallen, Switzerland
Michael.Gerfin@vwi.unibe.ch Michael.Lechner@unisg.ch, Heidi.Steiger@unisg.ch, www.vwi.unibe.ch/staff/gerfin www.siaw.unisg.ch/lechner
* Michael Lechner is also affiliated with CEPR, London, IZA, Bonn, ZEW, Mannheim. We are grateful to the State Secretariat of Economic Affairs of the Swiss Government (seco) and the Bundesamt für Sozialversicherung for providing the data. Financial support from the Swiss National Science Foundation (610-062887.00, Gerfin, and 4043-058311, 4045-050673, Lechner, Steiger) is gratefully acknowledged. The paper has been presented at seminars and workshops in Dublin (UCD), Konstanz, London (PSI), Mannheim, Syracuse and Washington (Univeristy of Maryland), as well as at the North American Summer Meeting of the Econometric Society in Los Angeles, the 10th International Conference on Panel Data in Berlin, the Transatlantic Labour Workshop and the European Summer Symposium in Labour Economics in Buchs, the meetings of the Econometric Society and the European Economic Association in Venice, the European Association of Labour Economics meeting in Paris, and the meeting of the German Economic Association in Innsbruck. We thank participants, in particular Orley Ashenfelter, Dan Black, Christian Dustmann, Maria Guadalupe, John Ham, Tom Knieser, Friedhelm Pfeiffer, and Jeff Smith for comments that helped to clarify important issues. All remaining errors and omissions are our own.
Abstract
Subsidised employment is an important tool of active labour market policies to improve the reemployment chances of the unemployed. Using unusually informative individual data from administrative records we investigate the effects of two different schemes of subsidised temporary employment implemented in Switzerland: non-profit employment programmes (EP) and a subsidy for temporary jobs (TEMP) in private and public firms. Econometric matching methods show that TEMP is more successful than EP in getting the unemployed back to work. Compared to not participating in any programme EP and TEMP are ineffective for unemployed who find jobs easily anyway or have a short unemployment spell. For potential and actual long term unemployed both programmes may have positive effects, but the effect of TEMP is larger.
Keywords
Subsidised temporary job, employment programme, temporary work contracts, active labour market policies, matching on the propensity score, Switzerland
JEL classification:
J38, J681 Introduction
Subsidised employment is an important tool of labour market policy in many developed countries. It exists not only in countries using the 'European' type of a more interventionist approach to labour market policy (like France, Germany, Sweden, ...), but it is also used by countries firmly based on the Anglo-Saxon model of the labour market, like the USA (Earned Income Tax Credit, EITC), Great Britain (as part of the new deal), and Canada (the Targeted Wage Subsidies and the Self-sufficiency Project).
There are considerable differences in the design of the subsidy schemes. The most common form is a wage subsidy with possibly some additional allowance for the employers and / or employees fixed cost. It may be paid either to the employer, the employee, or to both. The subsidy itself may be permanent (con- ditional on low earnings, like the EITC), or it may have a maximum eligibility period. The programmes may be directed at subsidising strictly temporary employment or just decreasing the initial wage to be paid by the employer for a job that is supposed to become a permanent one. Furthermore, within the subsidy schemes for temporary jobs: It could be a 'real' job in a firm operating in competitive markets, or the sub- sidised job may be in some specialised non-profit operation active in some sheltered part of the economy.
It is this difference that is the main focus of this paper. Finally, the actual direct cost of programme par- ticipation to individuals, the unemployment insurance system, and society as a whole may also differ substantially between the different schemes.
Even in the case of subsidies for temporary jobs different ways to implement the subsidy may influence its effects. These types of subsidies offer temporary employment that otherwise would not be accepted by the unemployed or would not be created by firms. The reasons could be the access to unemployment and wel- fare benefits for the unemployed or minimum employment costs (minimum wages, unionised sector, or other restrictions increasing wage costs) on the side of the employer. So far few theoretical and compara- tive empirical research has been devoted to analyse differential effects of different employment subsidies.1 A major reason is probably that usually cross-country studies would be needed to compare the different schemes. However, cross-country studies face the substantial problem of comparing two programmes under potentially very different labour market conditions. Therefore, it would be useful to 'partial out' the effect of local labour market conditions by comparing different programmes within the same country that are accessible to the same group of unemployed. Furthermore, a large and informative data base is necessary to address the selection issues that pop up in every evaluation study. This is a particularly
1 An exception are experimental studies in the USA which compared wage subsidies paid to the employee and the employer, respectively (Burtless, 1985, Woodbury and Spiegelman, 1987) and two recent studies of the Swedish active labour market policy (Carling and Richardson, 2001, and Sianesi, 2002) that however address somewhat different issues.
demanding task when one concentrates on the more subtle differences between two programmes that may be only small parts of the usually diverse active labour market policy a particular country runs.
Switzerland can be used to study the differences between two versions of employment subsidies that both operate on a larger scale and both are targeted at more or less the same population of unemployed. Both programmes use subsidised temporary employment to increase the reemployment chances of their partici- pants. The crucial difference is that one programme operates as a non-profit employment programme, whereas the other one subsidises temporary jobs in firms operating in a competitive environment.
Furthermore, in the Swiss case a large and exceptionally informative individual data base (coming from various administrative registers) is available that was previously used by Gerfin and Lechner (2002, GL hereafter) for a microeconometric evaluation study of several active labour market policies. Although not of primary interest in their study, GL already note a substantial difference between the effects of the two types of temporary employment subsidies. Recent evaluation studies of the Swedish active labour market policies, for example, also draw the general conclusion that programmes most closely attached to a 'real' job in a competitive environment dominate other programmes (Carling and Richardson, 2001, and Sianesi, 2001). The main difference to the Swedish subsidised jobs programme is that subsidised jobs are expected to become permanent in Sweden, while in Switzerland they are expected to be temporary.
Our empirical findings based on matching methods strongly confirm the positive effects of the subsidy for temporary jobs (TEMP) as compared to the employment programme (EP) type of the subsidy on average.
We dismiss the concern that the positive effects are due to the participants of TEMP taking up inferior, i.e.
jobs paying less than the jobs subsequent to the current unemployment spell. With respect to possible differential effects of the programmes for different groups of unemployed we find both programmes to be more effective in raising reemployment probabilities for the unemployed having substantial difficulties in the labour market. However, even for the 'better risks' TEMP seems to have some, albeit small positive effects. It appears that TEMP and EP are adding human capital, although with different effectiveness.
However, we cannot rule out that signalling effects also play a role in explaining our results as well. In terms of direct costs of the programmes TEMP is much less costly than EP (and not participating in any programme), which adds to the positive assessment of TEMP.
In summary, this paper contributes to the literature in several ways: First, it considerably extends the data base used by GL allowing a more detailed analysis of the outcomes achieved by the programmes. The time horizon is extended and other variables measuring the quality of employment are now available. Sec- ond, by focussing on two specific and similar types of programmes we are able to analysis their difference in participant selection and the resulting outcomes in a much more profound and informative way. We relate these differences to the different institutional set-ups and discuss theoretical implications. We check
whether the expected effect heterogeneity resulting from these considerations can actually be found in the data.
The structure of the paper is as follows: The following section describes the Swiss programmes in some depth. Section 3 briefly reviews theoretical concepts on why these different types of programmes may have different effects for different individuals. Section 4 as well as Appendix A describe the data and pre- sent some descriptive results for the differences between the different groups of participants. Section 5 gives a summary of the econometrics used, which is a multiple treatment evaluation framework using a 'matching on the propensity score' estimator. Section 6 presents the results and Section 7 concludes. Ap- pendix B contains the results of the estimation of the propensity scores in a multinomial probit framework.
Appendix C describes the extent of the common support problem and our remedies. Appendix D adds results concerning the subgroup heterogeneity of the effects.
2 Subsidised employment as part of active labour market policies:
the Swiss case
As already noted subsidised employment can take many forms. Switzerland uses two different types of subsidies to foster reintegration of the unemployment into the labour market. To understand the effects of the programmes and the composition of the different groups of participants, it is necessary to understand the specifics of the programme as well as the institutional environment in which they operate.
Swiss unemployment insurance
The basic rules of the Swiss unemployment insurance (UI) in the period of interest are as follows: Benefit entitlement lasts for a maximum of two years (conditional on employment history). The entitlement period consists of two parts: in the initial 30 weeks benefits are unconditional on programme participation, the remaining entitlement is in principle conditional on some participation. The benefit level in the two pe- riods is the same. However, in practice these rules are not strictly enforced: It is not unusual to participate in a programme in the first 30 weeks. More frequently, unemployed receive the benefits in the conditional period without any participation in ALMP, because no programme is offered. The entitlement is condi- tional on a previous contribution to the unemployment insurance for at least 6 months within the past two years. After the two year entitlement period expires, receiving a new entitlement period is conditional on being employed for at least 12 months within three years after the end of the previous unemployment
spell. The replacement ratio is usually 80% of the insured earnings, depending on socio-demographic characteristics.2 The maximum monthly benefit is about CHF 7000.
Switzerland runs a substantial and diverse active labour market policy.3 The active labour market pro- grammes (ALMP) in Switzerland can be grouped into three categories: a) training, b) employment pro- grammes, and c) subsidised temporary jobs. Training consists of a wide variety of courses, ranging from basic courses to specific work-related training. The differences between b) and c) are discussed below in detail.
A specificity of the Swiss system of active labour market policy is that the cantons are obliged by law to fill a minimum number of places per year. Until January 2000 the nation-wide minimum was 25'000 yearly places distributed across cantons according to their unemployment rates in the previous year. By comparison, the number of registered job seekers was about 190'000 in 1997 and 140'000 in 1998.
Employment Programmes (EP)
Employment programmes are offered by both public and private institutions. There is no substantial dif- ference between the type of 'jobs' offered by the two different groups of providers of these programmes.
The usual individual participation duration is about six months. There are two different types of pro- grammes: either it is a single position, i.e. a special job in a public organisation (e.g. administration or hospitals), or it is a collective programme. Collective programmes are carried out by specialised non-profit organisations. The jobs should be as similar as possible to regular employment, but they should be extraordinary, i.e. the organisers of employment programmes should not be in competition with other firms. However, in practice some organising firms may operate on the same market as other private firms with comparable products (e.g. in the repair and restoration sector). Collective employment programmes are regulated by the cantonal unemployment offices in consultation with the employer and the employee organisations.4 In conclusion, employment programmes can be seen as fully subsidised labour in a non- profit organisation. In most cases the subsidy even exceeds 100%, because some of the costs of capital, overhead costs, and so on may be reimbursed as well.
Unemployed are placed in employment programmes by the labour office. Participation is compulsory.
Interviews we conducted at the placement offices strongly suggest that it is not unusual that case workers use employment programmes as a test for the willingness to work. While participating in an EP the unem- ployed has to continue job search and must accept any suitable job offer (a job would not be considered suitable if it pays less than current unemployment benefits, the working conditions are unacceptable, or if
2 The replacement ratio is reduced to 70% if the unemployed does not have dependent family members to support.
3 More details can be found in GL and in Lalive, van Ours, and Zweimüller (2000).
the workplace is too far away from home). Formally, the organiser of the employment programme acts as the employer and the participant as an employee (but the organiser cannot “hire” the employees, they are selected by the placement office). Duration of the programme (usually 6 months), the wage and the social security contributions are regulated in a temporary work contract between the organiser and the worker. In particular, the organiser has to send a monthly payroll account to both the employee and the placement office. The participant is paid by the placement office. The wage has to be no less than the minimum wage set for the region and sector (if there is a collective wage agreement). It may exceed the level of the unemployment benefits, but in practice this is rather an exception. For the placement office there are no direct savings by placing an unemployed into EP. In 1998, roughly 17'000 persons participated in an employment programme (about 10% of the registered job seekers).
Swiss employment programmes are pretty similar to employment programmes in Germany. Other similar programmes are also operated in several other European countries. As in the Swiss case these types of programmes – if used at all – are usually an important part of the active labour market policy of that spe- cific country.
Subsidised Temporary jobs (TEMP)
The immediate objective of the subsidised temporary jobs programme is to encourage job seekers to accept job offers for “unsuitable” jobs (they pay less than their unemployment benefits) by overcompen- sating the difference with additional payments from the UI system. The income generated by this scheme is larger than unemployment benefits in case of not accepting the temporary job.5 Thus this programme is financially attractive for both the unemployed and the placement office. If the accumulated duration of temporary jobs within the entitlement period exceeds 12 months the unemployed becomes eligible for another 2-year entitlement period. However, insured earnings (to which the replacement ratio is applied to) are related to the wage earned in the temporary job which is below 80% of previous insured earnings (thus combining many such spells of TEMP would lead to a consistent decline in income). Mean duration of these temporary jobs is roughly 4 months, but there is considerable variation. The wording of the law regulating TEMP is not very specific. Rehiring laid-off workers in TEMP jobs by the same firm is usually not possible. But using TEMP as a subsidised screening device for firms is not ruled out and obviously
4 This so-called “three party commission” has the authority to decide whether an employment programme should be considered to be in competition with the private sector. It acts upon complaints by the private sector.
5 The compensation payment is the replacement ratio applied to the difference between the earnings in the temporary job and the previous earnings which will always be larger than the difference between the unemployment benefit and the earnings in the temporary job. At the same time the unemployment insurance system 'saves money' by always paying less than the regular unemployment benefits.
sometimes endorsed by the placement offices in order to improve job matches. However, TEMP jobs are not explicitly expected to become permanent after the subsidy runs out.6
Although TEMP is not part of the ALMP, roughly 20% (1998) of the unemployed participated at some point in TEMP. Bauer, Baumann, and Künzi (1999) report that only about 20% of the jobs in TEMP are arranged by the placement office. Employer and employee have a regular temporary work contract defin- ing the conditions of the job (mainly duration of the contract, wage and contributions to future pensions).
The wage cannot be below the above mentioned minimum wage. Since the wage has to be less than 80%
of previous earnings (the unemployment benefit) to be eligible for a subsidised temporary job, many jobs in TEMPare below the qualification level of the unemployed.
OECD (1996) states concerns that TEMP may lead to distortions in the labour market if it is not tightly monitored. For example, workers might be laid-off and recalled in the TEMP scheme. Furthermore, firms might use TEMP to avoid the dismissal protection rules to increase the flexibility of their work force, or TEMP might be used to avoid the wage levels set in collective wage bargaining agreements. However, so far there appears to be no evidence of abuse of TEMP in these respects.
An important feature of subsidised temporary jobs compared to EP is that TEMP is not part of the official ALMP (probably for historical reasons). Thus, places provided by TEMP are not counted towards the minimum of ALMP places to be filled per canton. It is also important to recognise that the main difference between TEMP and EP is the type of job and work experience they generate. Ignoring any potential mar- ket distortions and assuming that EP does not produce public goods to a considerable extent, then from the point of view of the taxpayer EP is more expensive than TEMP. An interesting question we look at with our data is whether these programmes are systematically used by case workers in the labour office for different groups of people (case workers fully control access to EP, but only approve of participation in TEMP).
Arrangements subsidising jobs within firms competing in the market that are not expected to become per- manent are not commonly used in European active labour market policies. One programme that is similar to TEMP is the Targeted Wage Subsidies scheme introduced 1996 in Canada. It is an employer based subsidy. A maximum of 60% of the wage is paid up to 78 weeks. Similar to the Swiss case the main goal of this programme is to offer work experience, not necessarily continuing employment, to the unem- ployed.
6 The original intention of policymakers was twofold: on the one hand there was the belief that working is better than not working, hence the provision of temporary jobs for the unemployed. On the other hand, the intention was to provide firms with a flexible workforce for temporary jobs, for which otherwise no suitable labour supply is
3 Why and for whom should these programmes work?
The main purpose of this paper is to answer the question whether and why the subsidised temporary job programme could be superior to the employment programme as indicated by previous results. In the fol- lowing we discuss three main reasons why the different programmes may have different effects: a) human capital, b) signalling and stigma, c) improved job matching.
Human capital
Both programmes do not incorporate explicit training, except for on-the-job training. Bell, Blundell and van Reenen (1999) show that the only way that a temporary subsidy can have a permanent effect on the employability of low-skilled unemployed is to raise their productivity through work experience in the programme. It is possible that both programmes do have this effect on productivity. However, given the institutional differences it is possible that employment programmes generate human capital that is less valued by potential employers due to the requirement that these jobs have to be “extraordinary” and not in competition with “real” jobs. Furthermore, being a real job TEMP may have stronger effects on “soft”
human capital such as important contacts and references, which can be very helpful in finding permanent jobs. This effect will be especially strong when the potential permanent job is in the same sector as the subsidised temporary job.
Signalling
Subsidised temporary jobs are often below the qualification of the unemployed (they usually pay less than unemployment benefits which are only 80% of previous earnings). Hence, it may be argued that human capital effects cannot be strong. However, it is possible that the programmes have a signalling value to employers. Because the subsidised temporary jobs are “real” jobs the employer may use this information to conclude that participants in TEMP are better in the sense of having a closer attachment to the labour force. The signal is especially valuable when the potential permanent job is in the same sector as the sub- sidised temporary job. In order to be a credible signal temporary subsidised jobs must be more costly to find for less productive workers. Since these jobs are limited and usually arranged by the unemployed themselves, hence requiring additional search efforts, this requirement appears to be fulfilled.
Signalling may also occur in terms of stigma effects. Suppose employment programmes are stigmatised in the sense that there is a common belief among employers that participants in employment programmes are on average less productive than their counterparts in subsidised temporary jobs. If the unemployed know this, more productive unemployed self-select themselves into the temporary subsidised jobs programme.
There is anecdotal evidence that employment programmes indeed carry the described stigma. Given this,
all unemployed would want to participate in TEMP, but again finding a subsidised temporary job is more costly for less productive unemployed.
Improved job matching
Firms may use TEMP as a subsidised screening device. Hiring new workers is costly and involves uncer- tainty about the quality of the applicants, especially when they come from unemployment. TEMP reduces these costs and uncertainties. Similarly, the unemployed may use TEMP to gain knowledge about poten- tial new employers or even new occupations. Taken together, these strategies may improve the matching process on the low-skill labour market. It is unlikely that EP will have a similar effect.
Discussion of the resulting effect heterogeneity
It is not possible to derive strict tests for the relative importance of these explanations. Nevertheless, we can think of hypotheses about effect heterogeneity for different groups of unemployed that are plausible under some explanations and not plausible under others. Examining the empirical evidence for these hy- potheses may indicate some answers to the question why programmes have different effects. Our strategy is to use nonparticipation in any programme as a benchmark because nonparticipation will have neither human capital nor lock-in effects. Different effects with respect to nonparticipation for different groups of unemployed may give some indication on why and for whom the two programmes TEMP and EP work.
Assume for the sake of the following arguments that if human capital is generated by one of the program- mes it is by replacing already lost or preserving human capital due to ongoing unemployment. Consider the expected effects of employment programmes and subsidised temporary employment compared to nonparticipation for unemployed with a short unemployment spell. For this group, we expect the human capital effects of the programme to be negligible.7 On the other hand, lock-in effects are particularly strong for this group because at the beginning of the unemployment spell the job offer arrival rate is rela- tively high. Hence, if a programme has a positive effect with respect to nonparticipation it should be primarily due to a signal. On the other hand, for the long term unemployed we expect human capital ef- fects and much weaker lock-in effects. Signalling may be important as well but it is not possible to disen- tangle the reasons for the estimated effects.
Next we consider effect heterogeneity with respect to the skill level of the unemployed. This is the case where the signalling models of McCormick (1990) and Ma and Weiss (1993) are most appropriate in our setting. Ma and Weiss (1993) show that in case of job loss it may be better to become unemployed than to take up a low-skill job. Taking up a low-skill (“lousy”) job may be seen as a bad signal by future employ-
ers. A similar argument is made in McCormick (1990). Most subsidised temporary jobs and temporary employment are below the qualification of the unemployed. In this case the theoretical models imply that for the better qualified unemployed it is optimal not to take up such a job because it would be a negative signal to do so. Hence we should find negative effects with respect to nonparticipation when signalling is important. For those with relatively low earnings (and presumably productivity), on the other hand, this negative signalling effect should not be important. Any effects we find could be due to human capital or signalling, where the signalling effect is different from the one above. It may be positive for TEMP (the unemployed shows motivation) and negative for EP (stigma). A similar argument applies to qualification measured by the case worker’s evaluation of the chances to find a job.
To understand which group of unemployed should be expected in which programme, it is instructive to compare the different incentive structures generated by the two programmes for the direct actors, namely the unemployed as well as the local placement office. From the point of view of the latter it is obvious that subsidised temporary jobs are attractive. The direct costs are lower and they do not require assignment efforts as they are in many cases found by the unemployed. The case workers basic strategy appears to be to wait and see whether the unemployed finds a regular job quickly. If the unemployed finds neither a regular nor a subsidised temporary job the case worker tries to find a suitable programme. Again, our in- formal interviews suggest that the unemployed are sent to employment programmes when nothing else seems to be appropriate. As already mentioned, sometimes employment programmes are also used as a test for the willingness to work. This behaviour is indicative of a rather bad reputation the employment programmes may have with potential employers. Another reason to send unemployed to employment programmes is the requirement that each canton has to fulfil its quota of programme places (c.f. Section 2).
For the unemployed the situation is more complicated. The above considerations suggest the following pattern: at the beginning of the unemployment spell it is not optimal to do low skill jobs while looking for an adequate job, especially for better qualified unemployed. In addition, an indirect effect of participating in a programme could be a reduction in job search activities and job offers from the placement office compared to nonparticipants. The unemployed with good chances to find a job will want to avoid this. Af- ter some time in unemployment, however, it can become optimal to search for a temporary job. However, the fact that the majority of subsidised temporary jobs is arranged by the unemployed herself suggests that a search effort is needed to get into this programme. This in turn implies that it is costly for the unem- ployed to find these programmes. Both human capital and the signalling explanations of the effects imply that this cost is only taken when the expected return is higher. Another incentive to enter the temporary
7 It is well known from the research on duration dependence and hysteresis that one effect of ongoing unemployment is an increasing depreciation of human capital.
subsidised job programme is job shopping of the unemployed. In other words, the unemployed uses the subsidised jobs to improve his chances of a good job match.
How will these considerations affect the composition of the participants in both programmes? In fact, it seems that nobody has an incentive to get into an employment programme (except for case workers in order to fulfil their quota). The described strategy of the case workers suggests that participants in em- ployment programmes have a relatively long unemployment duration when they enter the programme.
Unemployed with sanctions regarding their benefit already imposed in the past may also end up in em- ployment programmes, given that these are sometimes used as a test for the willingness to work. Further- more, we would expect the unemployed with low skills and low chances to find a job to be overrepre- sented in the employment programme because it is difficult for them to find subsidised temporary jobs.
4 Data and descriptive statistics
4.1 Data base
Our empirical analysis is based on two matched sources of administrative data that have already been used by GL. By the usual international standards for observational evaluation studies, this data set is exception- ally informative. The first data source is the information system for placement and labour market statistics (AVAM) and the unemployment offices payment systems (ASAL). We have data from January 1996 to December 1999 for everybody who is registered as unemployed on December 31, 1997. These data pro- vide detailed information about the unemployment history, ALMP participation and personal charac- teristics. For a random subsample of about 30'000 observations we obtained the social security records for the period 1988-1999. The merged sample contains information on the individual labour market histories and earnings on a monthly basis for 10 years prior to the current unemployment spell. In addition we have detailed information concerning several aspects: socio-demographics (age, gender, marital status, native language, nationality, type of work permit, language skills), region (town/village and labour office in charge), subjective valuations of the placement officer (qualifications, chances to find job), sanctions im- posed by the placement office; previous job and desired job (occupation, sector, position, earnings, full- / part-time), and a short history of labour market status on a daily basis. Particularly the subjective valuations of the placement officers and the benefit sanctions can be informative since they capture char- acteristics like motivation and personal appearance that are usually unobservable.
Compared to GL there are important extensions to the data. We now have social security data for the years 1998 and 1999 which allows us to construct additional and more precise outcome variables for employ- ment and earnings on a monthly basis. In GL the most important outcome variable used to measure the ef-
fects of the programme was leaving unemployment towards employment as recorded in the unemployment register. Now, we measure employment by the entries in the social security data. Hence, we construct variables measuring the quality of employment in terms of earnings and to some extent job duration. This allows us to address the question whether specific programmes lead to types of employment that may be of lower "quality" than the job prior to the current unemployment spell. Furthermore, we evaluate the effects on earnings per se. Given the new data we evaluate the effects up to 24 months after the pro- grammes start. More details on the data can be found in Appendix A.
4.2 The definition of programmes used in the empirical analysis
We differentiate four groups of ALMP participation states to which we allocate all observations. Since we are not interested in courses per se we aggregated the 16 different training courses into one broad group.
Employment programmes are not differentiated according to whether they are offered by public or private institutions as in GL, because our earlier study found no systematic differences of the effects of these two similar forms of employment programmes. The third programme category covers participants in subsidised temporary jobs, and the final (comparison) group consists of those who did not participate in any major programme between January and December 1998.8 A major programme is defined as having a duration of at least two weeks. Following the arguments in GL we evaluate only the first major pro- gramme starting between January and December 1998 (see that paper for details).9
For the group of nonparticipants important time varying variables like 'unemployment duration prior to the programme' are not defined. To make meaningful comparisons to those unemployed entering a pro- gramme, we use an approach suggested in Lechner (2002b): For each nonparticipant a hypothetical pro- gramme starting date is predicted by relevant information available in Dec, 1997. Persons with predicted starting date later than their actual exit date from unemployment are excluded from the data set.
4.3 The sample
We apply a series of sample selection rules to the data. Full details are given in Appendix A.1. The most important selection criteria are that we consider only individuals unemployed (without any other part-time job) on Dec 31, 1997 with an unemployment spell at that time of less than 12 months who have not par- ticipated in any major programme in 1997 and who are between 25 and 55 years old. The reasons for these selection criteria are that -given the two-year entitlement period- we want to make sure that there is suffi-
8 The reason not to consider programmes starting before 1998 is that the data does not contain sufficient information on the type and the duration of programmes prior to 1998. Comprehensive coverage of labour market programmes in the official statistics was only introduced in 1998.
9 In practice this approach is less restrictive than it appears. Only about 30% of all participants enter a second programme, and the majority of these successive programs are of the same type as the first programme.
cient time left to participate in a programme after December 31, 1997. Furthermore, since our focus is on the first programme we exclude those who participated in a major programme before 1998. In addition given the variety of options for the young (schooling) and the older unemployed (early retirement) we exclude them from our analysis. The final data set has 18’354 observations. For detailed descriptive sta- tistics the interested reader is referred to Table A.3 in Appendix A. 10
4.4 Descriptive comparison of programme groups
Table 1 shows selected descriptive statistics to compare participant groups in the different programmes.
Our main interest is the comparison of employment programmes and subsidised temporary jobs; the se- lected variables show significant differences between the two groups of participants. Our previous argu- ments in Section 3 were based on two central attributes: skill level and unemployment duration. The participants in employment programmes are clearly the least skilled, measured by the chances to find a job, qualification, job position, and previous earnings. For the other two groups there are hardly any dif- ferences, with the exception of the chances to find a job which are favourable for the participants of the subsidised temporary job programme. Unemployment spell duration at the time of programme start is almost three months larger for participants in employment programmes reflecting case workers’ tendency to send unemployed to these programmes when no other programme seems to be adequate. Hence, the numbers support our expectations derived from the above discussions: we find significantly more low skilled unemployed in employment programmes, and subsidised temporary jobs take place earlier in the unemployment spell. This is in accordance with the hypothesis that there is a limited supply of temporary jobs. The low skilled may have problems finding them and after some time the case workers allocate them to employment programmes. Furthermore, there is obvious regional and occupational heterogeneity in the composition of participants in the programmes. The political structure of Switzerland gives the cantons a considerable degree of autonomy. They may put different emphasis on the various programmes in their local implementation of the national ALMP. 11
10 Compared to GL the number of participants in TEMPis larger. This is due to a change in the definition of a major programme in the case of TEMP. In the earlier study the proportion of the time spent in TEMPrelative to the month was set to 66% in order to be counted as a month in TEMP; In this study we reduced this threshold to 50%.
11 This is of course only a rough descriptive comparison, not a complete analysis of the participants structure. The results of a multinomial probit for the selection procedure used in a later stage to estimate propensity scores can
Table 1: Number of observations and selected characteristics of different programme groups
Group Subsidised
temporary job (TEMP)
Employment programmes (EP)
Nonparticipation (NONP)
Obs. (persons) 5365 2107 5461
Pre-programme characteristics
Chances to find a job good or very good 23 18 22
difficult or special case
(share in %) 13 24 18
Qualification (mean) 1.74 1.87 1.73
Job position very low 37 47 36
High 5 3 7
Unemployment duration before programme (mean days) 222 303 218
Female 41 38 43
Nationality foreign with yearly permit 15 18 16
foreign with permanent permit 31 29 31
Swiss 54 53 53
Earnings before unemployment (mean per month in CHF) 3970 3660 3950
Region Zurich 18 17 22
West 21 29 16
Eastern 10 7 9
Central 5 7 5
South-west 28 22 30
North-west 11 9 9
Post-programme outcomes
Earnings Sept. 1999 (if employed) (mean in CHF) 3672 3279 3702
Employed Sept. 1999 (share in %) 72 58 59
Note: Qualification is measured as skilled (1), semiskilled (2), and unskilled (3).
Comparing the outcomes of the participants in the programmes we find that earnings in September 1999 are almost identical for nonparticipants and participants in temporary subsidised job, but lower for par- ticipants in employment programmes. The employment share is on a similar level for all programmes ex- cept for temporary wage subsidies where it is more than ten percentage points higher. Of course, these figures for the outcome variables cannot be interpreted as the causal effects of the programmes.
5 Econometrics
We base our analysis of the prototypical model of the microeconometric evaluation literature with multi- ple treatments: An individual chooses between several states, like participation in an employment pro- gramme or non-participation in such a programme. The potential participant in a programme gets an hypo- thetical outcome (e.g. earnings) in both states. This model is based on the binary potential outcome model (Roy, 1951, Rubin, 1974) extended by Imbens (2000) and Lechner (2001) to multiple, mutually exclusive states. Here, we consider outcomes of four different states denoted by { , ,Y Y Y Y0 1 2, }3 . The different states are called treatments in the following to stick to the terminology of that literature. For any individ-
ual, only one component of { , ,Y Y Y Y0 1 2, }3 is observable. Participation in a particular treatment m is indicated by the realisation of the random variable S,SÎ{0,1, 2,3}. This notation allows us under the usual assumptions (see Rubin, 1974) to define average treatment effects for pair-wise comparisons of the effects of different states:
g0
m l, =E Y( m-Yl)=EYm-EYl; (1)
q0m l, = E Y( m-Y S ml| = )=E Y S m( m| = )-E Y S m( |l = ); m l m l¹ ; , Î{0,1, 2,3}. (2)
g0m l, denotes the expected (average) effect of treatment m relative to treatment l for a participant drawn randomly from the population (average treatment effect, ATE).12 ATE’s are symmetric (g0m l, = -g0l m, ).
q0m l, is the expected effect for an individual randomly drawn from the population of participants in treat- ment m only (ATE on the treated, ATET). ATET’s are not symmetric, if participants in treatments m and l differ in a way that is related to the distribution of X, and if the treatment effects vary with X.
5.1 Identification
ATE’s and ATET’s are generally not identified so that additional assumptions are needed. We already noted that our data compiled from different administrative records are so rich that it seems plausible to assume that we observe all important factors that jointly influence labour market outcomes and the process selecting people into the four different states. Therefore, we assume that treatment participation and treatment outcome is independent conditional on a set of (observable) attributes (conditional independence assumption, CIA). CIA defined to be valid in a subspace cof the attribute space is formalised in expression (3):
Y Y0, ,...,1 YMCS X| = " Îx, x c. (3)
This assumption requires the researcher to observe all characteristics that jointly influence the outcomes as well as selection into treatments. In addition CIA requires that all individuals that are part of the evalua- tion could participate in all states (i.e. 0<P S m X( = | =x), " =m 0,...,3, " Îx c).
Equation (3) postulates that conditional on the observable attributes there remains no systematic selection on unobservables. In other words there are no exogenous variables left out that are both correlated with
potential outcomes and the participation decision. Candidates for such unobservables include variables like motivation, ability, and personal appearance. Our unusually informative data allows us to capture the major effects of these unobservables. For example, motivation can be measured by sanctions imposed by the placement office as well as by the employment history in the past ten years. Unobserved ability is captured by past earnings, and specific labour-related problems can be measured by past employment profiles (repeated movement between labour market states). Of particular importance is the variable
“chances to find a job”, which is a subjective judgement by the placement officer. This judgement is based on interviews and the impressions the placement officer obtains in his interviews in the beginning of the unemployment spell. This variable should capture characteristics like motivation and personal appearance that are usually unobservable. After controlling for this wealth of information there should be little unobserved heterogeneity left that is systematically correlated with labour market outcomes and pro- gramme participation. For detailed arguments about identification the reader is referred to GL.
5.2 A matching estimator
Lechner (2001) shows that CIA identifies all effects defined in this section and that expression (3) implies independence not only conditional on X but also conditional on the marginal probabilities of the states (conditional on X), denoted by [ ( ), ( ),P X P X P X P X0 1 2( ), 3( )].13 Based on this insight, Lechner (2001, 2002a, b) propose and apply different matching estimators for that problem. Here we use the version implemented in the paper by GL (see Table 2 for details).
Several comments are in order: A discussion of the implementation as well as the results of the simulated maximum likelihood estimator of the multinomial probit model used in Step 1 is given in Appendix B.
Step 2 ensures that we estimate only effects in regions of the attribute space where two observations from two treatments could be observed having a similar participation probability.14 Otherwise the estimator will give biased results (see Heckman, Ichimura, Smith, Todd, 1998). In total the common support criteria discarded only about 3.5% of the observations (see Appendix C for details).
12 If a variable Z cannot be changed by the effect of the treatment then all what follows is also valid in strata of the data defined by different values of Z.
13 Depending on the effect to be estimated we need to condition only on a subset or of functions of these probabilities. For all details the reader is referred to Lechner (2001).
14 This condition is also called the 'common-support requirement'. Note that if we would only be interested in pair- wise effects the current implementation would be unnecessarily strict, since making sure that there is an overlap for each pair would be sufficient. Our implementation has the advantage that we evaluate all programmes on the same support.
Table 2: A matching protocol for the estimation of g0m l, and 0,
qm l
Step 1 Specify and estimate a multinomial probit model to obtain [P x P x P x P xˆN0( ), ˆN1( ),ˆN3( ),ˆN4( )].
Step 2 Restrict sample to common support: Delete all observations with probabilities larger than the smallest maximum and smaller than the largest minimum of all subsamples defined by S.
Step 3 Estimate the respective (counterfactual) expectations of the outcome variables.
For a given value of m and l the following steps are performed:
a) Choose one observation in the subsample defined by participation in m and delete it from that pool.
b) Find an observation in the subsample of participants in l that is as close as possible to the one chosen in step a) in terms of [P x P xˆNm( ), ˆNl( )]. 'Closeness' is based on the Mahalanobis distance.
Do not remove that observation, so that it can be used again.
c) Repeat a) and b) until no participant in m is left.
d) Using the matched comparison group formed in c), compute the respective conditional expectation by the sample mean. Note that the same observations may appear more than once in that group.
Step 4 Repeat Step 3 for all combinations of m and l.
Step 5 Compute estimates of treatment effects using the results of Step 4 by means in matched samples.
Note: Lechner (2001) suggests an estimator of the asymptotic standard errors for gˆNm l, and ˆm l,
qN conditional on the estimated probabilities in Step 1.
A third remark about the matching algorithm concerns the fact that the same comparison observation is used repeatedly in forming the comparison group (matching with replacement). This modification of the 'standard' estimator (which means increasing the variance by reducing the bias) is necessary for the estimator to be applicable at all when the number of participants in treatment m is larger than in the com- parison treatment l. Since the role of m and l could be reversed in this framework, this is always the case when the number of participants is not equal in all treatments. For the sake of brevity we do not document the matching quality explicitly. Similarly to the already mentioned previous studies this estimator roughly balances the covariates in an appropriate way.
6 Empirical estimates of the effects
6.1 Measurement of the outcomes in the labour market
According to Swiss legislation the primary objective of the active labour market policy is to increase the reemployment probabilities. At least implicitly, the idea is also that the new job should be at least of similar quality as the previous one. We combine the two data sources to develop indicators that proxy these objectives. We compute indicators of successful employment from the social security data by using information whether there are payments from employment that can be related to a particular month. We define an employment spell as successful if it has a duration of at least 3 months. In addition we create an
indicator that measures the quality of employment (continuously employed for at least 3 months with earnings at least 90% of earnings in the previous job). Furthermore, as a crude proxy for individual pro- ductivity we include gross earnings coded as zero when an individual is not employed in the regular la- bour market. We also compute the months of unemployment within the next 12 months to get a measure on how many months of unemployment programme participation may save within a year. A final outcome variable (seeking a job) is based on the information whether somebody is registered with the labour office as job seeker. This outcome variable will pick up the institutional feature that subsidised temporary jobs can extend the unemployment benefit eligibility period. Thus the incentive to remain registered with the labour office is larger compared to other programmes, which do not affect the two-year eligibility period.15 Table A.2 in Appendix A describes the outcome variables more precisely. Table A.3 shows descriptive statistics.
We measure the effects of a programme in the month after the programme started (with simulated begin- ning dates for nonparticipants, see Section 4.2). In case an individual is known to be informed about pro- gramme participation prior to the month of the actual start of the programme we use this month of infor- mation as start date, because a programme may have an effect on individual behaviour from the very mo- ment when an individual knows that she will participate. Furthermore, focusing on the begin of the pro- gramme rules out that programmes appear to be successful, just because they keep their participants busy by making them stay in the programme. We consider a programme to be most successful if everybody would leave for employment (of 'good' quality) immediately after she is informed about future participa- tion. We must emphasise that whenever a person participates in any of the programmes she is considered as registered unemployed (and not employed) in the definition of all outcome variables.
For programmes starting in January 1998 we measure outcome variables for 23 months (2/1998-12/1999) or 12 months for the accumulated measures. However, since the evaluated programmes may start between Jan. 1998 and Dec. 1998, only 12 months of nonaccumulated outcomes are observable for everybody.
Since a large share of the programmes started in the first quarter of 1998, for most observations we meas- ure the effects for at least 18 months. When interpreting the result we should also keep in mind that the economy came out of the 1997 recession fairly quickly in 1998 and particularly in 1999 leading to a drop in the unemployment rate from 5.0% in December 1997 (share of unemployment spells longer than 1 year:
33%) to 2.5% in December 1999 (21%).
15 It is possible to remain registered when the eligibility period is over, but of course there will be no more unem- ployment benefits.
6.2 Mean effects of programmes for their participants
Table 3 shows the means of the outcomes in the various groups, the estimated counterfactual expectations and pair-wise comparisons between the subsidy programmes and between the programmes and nonpar- ticipation. For the sake of brevity COURSES are omitted because their effects are not central to this paper.
Furthermore, we concentrate on three outcome variables: employment during at least 3 consecutive months generating on average more than 90% of previous monthly earnings, average monthly earnings, and the number of additional unemployment over the next 12 months. For the first two outcomes we pre- sent results 3, 9, 15 and 21 months after the begin of the programme, for the third outcome that is based on accumulating effects over the 12 months ahead, we present effects 1 and 6 months after the start. Column (3) and (4) give the exact sample sizes (after imposing common support) available at each point of (proc- ess) time. Note that sample sizes fall after month 12 (the last month observed is month 24, programme participation starts between month 1 and 12). Thus, the population changes somewhat after month 12 in the sense that, for example, the estimate for month 23 is entirely based on individuals entering the pro- gramme in January 1998. Therefore, the precision of the estimates falls as well, which is reflected in the estimated standard errors increasing after month 12.
Columns (5) and (8) show the mean outcomes for the participants in programme one (5) as well as the mean outcomes for participants in programme zero (8). Column (6) shows the estimated mean counter- factual outcome of treatment one for population zero. Column (7) shows the respective estimated mean counterfactual outcome of treatment zero for population one. In general, all variables are increasing be- cause more and more of the unemployed find jobs the longer the duration of unemployment (and only a few return to unemployment), which is not surprising given the economic upswing during 1998 and 1999.
Nevertheless the pattern of the increase varies considerably between different treatments and different populations. The variation in earnings (coded as zero if the individual is not employed in the first labour market) is largely driven by the variation in employment status.
The comparison of column (5) to column (6) and of column (8) to column (7) reveals the magnitude of the selection bias corrected for by the estimation procedure. It is interesting to note that the selection bias would be largest in the comparison of EP and NONP and relatively modest in the comparisons involving TEMP. The comparisons clearly show that the unemployed in EP have the worst labour market perspec- tives in all potential states. From these estimates the estimated mean effects of two different states for participants in treatment 1 and 0 can be directly computed. These estimates and the corresponding as- ymptotic standard errors are given in columns (9) to (12). Columns (13) and (14) show the effects for the joint population of participants (TEMP, EP, courses) and the nonparticipants. When bold, effects are sig- nificant at the 1% level, when in italics they are significant at the 5% level.
Table 3: Estimates of effects Sample size Out-
come Month after
begin 1 0
( 1| E Y
|S=1) ( 1| E Y
|S=0) ( 0| E Y
|S=1) ( 0| E Y
|S=0) ˆ1,0
qN Std.
err.
ˆ0,1
q
- N Std.
err.
ˆ1,0
gN Std.
err.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)
Subsidised temporary job (1) compared to employment programme (0)
EWEL 3 5182 2085 17 15 9 8 8 1.2 8 1.3 7 1.1
in % 9 5182 2085 29 26 20 18 9 1.7 7 1.6 7 1.5
15 4846 1889 42 39 33 30 9 2.1 9 1.9 7 1.8
21 2925 778 45 43 38 34 7 3.2 8 2.9 7 2.6
EARN 3 1154 921 580 415 574 56 506 65 552 52
in 9 1832 1609 1213 1022 618 81 587 78 528 72
CHF 15 2491 2217 2073 1808 418 96 409 89 331 84
21 2716 2405 2421 2179 295 148 226 135 188 123
UE in 1-12 5182 2085 5.8 6.0 7.4 7.5 -1.6 0.2 -1.5 0.2 -1.7 0.1
months 6-17 4505 1640 3.9 3.9 4.8 4.8 -0.9 0.2 -0.9 0.2 -1.1 0.2
Subsidised temporary job (1) compared to nonparticipation (0)
EWEL 3 5182 5225 17 15 19 19 -2 1.1 -4 1.1 -2 0.9
in % 9 5182 5225 29 27 27 25 2 1.3 2 1.2 3 1.1
15 4846 5097 42 40 34 33 8 1.4 7 1.4 8 1.2
21 2925 3921 45 43 38 35 7 1.7 7 1.6 8 1.5
EARN 3 1154 1048 1244 1163 -90 59 -116 54 -58 49
in 9 1832 1799 1734 1633 98 67 166 61 193 55
CHF 15 2491 2348 2148 2055 343 73 293 66 359 60
21 2716 2601 2411 2211 305 85 389 79 359 73
UE in 1-12 5182 5173* 5.8 5.9 4.7 5.1 1.1 0.1 0.8 0.1 0.9 0.1
months 6-17 4505 4987 3.9 3.8 2.8 3.0 1.1 0.1 0.8 0.1 0.9 0.1
Employment programme (1) compared to nonparticipation (0)
EWEL 3 2085 5225 8 8 13 19 -6 1.5 -10 1.4 -9 1.1
in % 9 2085 5225 18 22 20 25 -1 1.7 -4 1.9 -4 1.5
15 1889 5097 30 32 27 33 3 2.0 -1 2.3 1 1.8
21 778 3921 34 37 29 35 6 2.7 1 3.0 1 2.6
EARN 3 415 528 796 1163 -381 76 -635 63 -610 54
in 9 1022 1273 1231 1633 -209 91 -359 91 -334 74
CHF 15 1808 2014 1621 2055 187 103 -40 106 28 87
21 2179 2436 1849 2211 330 137 225 143 170 122
UE in 1-12 2085 5173 7.5 7.7 5.3 5.1 2.2 0.2 2.6 0.2 2.6 0.1
months 6-17 1640 4987 4.8 5.0 3.0 3.0 1.8 0.2 2.0 0.2 2.0 0.2
Note: EWEL: Employed for at least 3 months with average earnings of more than 90% of previous earnings. EARN: Monthly gross earnings in employment with minimum duration of 3 months. UE: Months of registered unemployment in 12 months period. Results are based on matched samples (see Table 3). Bold numbers indicate significance at the 1%
level (2-sided test), numbers in italics indicate significance at the 5% level. Results for COURSES are available on re- quest. * 52 nonparticipants have a simulated starting date of Jan 1999.
The estimated effects show that TEMP is the superior programme. About 15 months after the begin of the programme we find a more or less stable and significant positive employment effect of participating in TEMP of about 7-9% points compared to EP and NONP. There does not appear to be too much variation of this effect between different populations defined by treatment status. Similarly, there is an average
earnings gain after 15 months of about 300-400 CHF. Comparing both programmes to nonparticipation reveals a particular shape: negative effects appear in the beginning that eventually get positive and signifi- cant. In the medium run it seems that both programmes increase the employment probabilities for their participants by about 6-7% points. However, even for the participants in EP it would have been more beneficial to enter TEMP instead. This view is confirmed when considering the accumulated effects: Par- ticipating in TEMP instead of EP reduces registered unemployment by about one month per year. For reasons already discussed in section 6.1, in comparison to nonparticipation both programmes increase unemployment benefit duration by about 1 (TEMP) to 2 months (EP) per year in the time immediately after the start of the programme.
Before returning to the dynamic shape of the effects in more detail, it is instructive to get an idea about the magnitude of earnings. If we assume that those not working would receive the mean earnings of those working, we are able to compute counterfactual earnings for the employed in all states by dividing the earnings displayed in Table 3 by the employment probability. Earnings computed that way (Table 4) suggest that the effects presented in Table 3 are mainly driven by employment effects. For example, for participants in TEMP the difference between potential earnings in TEMP and in EP is CHF 121 (treatment effect 15 months after programme start). Based on the different definition of the earnings variable (coded 0 for nonemployed), the corresponding treatment effect in Table 3 is 418, because it also includes an effect on employment. However, these numbers have to be interpreted with care because the assumption used to compute them is not very convincing: there may be considerable selection going on (of another type that the one already corrected for) due to different groups of unemployed entering employment at different times for different treatments. Furthermore, the estimates may be unreliable particularly for the smaller samples in the second year because dividing one estimated quantity by an another small estimated quantity (between 0 and 1) may result in very imprecise estimates.
Table 4: Average potential earnings for those who would be employed
Potential outcome TEMP TEMP TEMP EP EP EP NONP NONP NONP
Population TEMP EP NONP TEMP EP NONP TEMP EP NONP
Outcome month after begin
(3) (4) (5) (6) (7) (8) (9) (10) (11)
EARN 3 3596 3320 3453 3472 3069 3579 3707 3385 3738
in 9 3574 3309 3540 3534 3230 3755 3759 3266 3768
CHF 15 3729 3418 3611 3608 3275 3627 3703 3185 3735
21 3781 3421 3674 3675 3317 3733 3791 3320 3751
Note: Estimated mean earnings divided by estimated employment probability for respective population.
Although Table 3 already indicated the time shape of the effects, the following figures summarise the dynamics of the effects by showing their development over time after the start of the programme on a monthly base (if significant at the 5% level). Note again that the sample sizes decrease after 12 months.
The sample is probably large enough to estimate the effects for about 21 months after the start of a pro- gramme with sufficient precision.
Figure 1: Dynamics of average effects for participants in TEMP compared to EP, COURSES, and NONP after the start of the programme
Difference in %-points
Fig. 1a: Employment with duration ³ 3 months
Months after start
Fig. 1b: Searching for a job
Months after start
Difference in CHF / %-points
Fig. 1c: Earnings in employment ³ 3 months
Months after start
Fig. 1d: Employment without earnings loss
Months after start
Note: NONP: Nonparticipation; EP Employment programme; TEMP: Subsidised temporary job. Start dates for nonparticipants are simulated. Only estimated effects that are significant at the 5% level (two-sided test) are reported.
Figures 1 and 2 display the estimates of the effects of TEMP (compared to the other states) for participants in TEMP (Figure 1) as well as the effects of EP for the participants in EP (Figure 2) for 4 different out- come variables. A line above zero indicates that TEMP has a positive employment effect, or a positive effect on the probability to be registered as searching, respectively, relative to the programme associated
